Mass spectrometry-based metabolomics:a guide for annotation, quantification and best reporting practices

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics-derived data.</p

Single-Cell Approach Reveals Intercellular Heterogeneity in Phage-Producing Capacities

Bacteriophage burst size is the average number of phage virions released from infected bacterial cells, and its magnitude depends on the duration of an intracellular progeny accumulation phase. Burst size is often measured at the population level, not the single-cell level, and consequently, statistical moments are not commonly available. In this study, we estimated the bacteriophage lambda (ƛ) single-cell burst size mean and variance following different intracellular accumulation period durations by employing Escherichia coli lysogens bearing lysis-deficient ƛ prophages. Single lysogens can be isolated and chemically lysed at desired times following prophage induction to quantify progeny intracellular accumulation within individual cells. Our data showed that ƛ phage burst size initially increased exponentially with increased lysis time (i.e., period between induction and chemical lysis) and then saturated at longer lysis times. We also demonstrated that cell-to-cell variation, or “noise,” in lysis timing did not contribute significantly to burst size noise. The burst size noise remained constant with increasing mean burst size. The most likely explanation for the experimentally observed constant burst size noise was that cell-to-cell differences in burst size originated from intercellular heterogeneity in cellular capacities to produce phages. The mean burst size measured at different lysis times was positively correlated to cell volume, which may determine the cellular phage production capacity. However, experiments controlling for cell size indicated that there are other factors in addition to cell size that determine this cellular capacity

Computer-aided whole-cell design:taking a holistic approach by integrating synthetic with systems biology

Computer-aided design for synthetic biology promises to accelerate the rational and robust engineering of biological systems; it requires both detailed and quantitative mathematical and experimental models of the processes to (re)design, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. Computer-aided design strategies require quantitative representations of cells, able to capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems, and we discuss several challenges for the realization of our vision. The possibility to describe and build in silico whole-cells offers an opportunity to develop increasingly automatized, precise and accessible computer-aided design tools and strategies throughout novel interdisciplinary collaborations

Screening of candidate substrates and coupling ions of transporters by thermostability shift assays

Substrates of most transport proteins have not been identified, limiting our understanding of their role in physiology and disease. Traditional identification methods use transport assays with radioactive compounds, but they are technically challenging and many compounds are unavailable in radioactive form or are prohibitively expensive, precluding large-scale trials. Here, we present a high-throughput screening method that can identify candidate substrates from libraries of unlabeled compounds. The assay is based on the principle that transport proteins recognize substrates through specific interactions, which lead to enhanced stabilization of the transporter population in thermostability shift assays. Representatives of three different transporter (super)families were tested, which differ in structure as well as transport and ion coupling mechanisms. In each case, the substrates were identified correctly from a large set of chemically related compounds, including stereo-isoforms. In some cases, stabilization by substrate binding was enhanced further by ions, providing testable hypotheses on energy coupling mechanisms

Defining the RNA interactome by total RNA-associated protein purification

Abstract The RNA binding proteome (RBPome) was previously investigated using UV crosslinking and purification of poly(A)‐associated proteins. However, most cellular transcripts are not polyadenylated. We therefore developed total RNA‐associated protein purification (TRAPP) based on 254 nm UV crosslinking and purification of all RNA–protein complexes using silica beads. In a variant approach (PAR‐TRAPP), RNAs were labelled with 4‐thiouracil prior to 350 nm crosslinking. PAR‐TRAPP in yeast identified hundreds of RNA binding proteins, strongly enriched for canonical RBPs. In comparison, TRAPP identified many more proteins not expected to bind RNA, and this correlated strongly with protein abundance. Comparing TRAPP in yeast and E. coli showed apparent conservation of RNA binding by metabolic enzymes. Illustrating the value of total RBP purification, we discovered that the glycolytic enzyme enolase interacts with tRNAs. Exploiting PAR‐TRAPP to determine the effects of brief exposure to weak acid stress revealed specific changes in late 60S ribosome biogenesis. Furthermore, we identified the precise sites of crosslinking for hundreds of RNA–peptide conjugates, using iTRAPP, providing insights into potential regulation. We conclude that TRAPP is a widely applicable tool for RBPome characterization

NMR Metabolomics for Optimizing Cell-Free Protein Synthesis

Cell-Free Protein Synthesis (CFPS) has been utilized by biochemists to produce a variety of chemicals and therapeutics. While CFPS has spawned research in the biochemistry and medical communities, there are still unknown issues with interlaboratory variability with the technique. This work explored the black box nature of CFPS reactions by analyzing the CFPS reactions in situ with Nuclear Magnetic Resonance (NMR) spectroscopy. Aim 1 developed the protocol for conducting NMR experiments on E. coli cell-free reactions as well as a data analysis pipeline. This was accomplished with 1H NMR, capturing metabolite changes over time. The 1D NOESY experiment proved to provide good signal-to-noise for consecutive 9-minute acquisitions. Aim 2 explored the differences between lysates produced by different laboratories as well as how they behave in reactions. The proton data indicated there are detectable differences between the lysates as well as how they perform in reactions. Ethanol production in lysates and in reactions was a major difference between lysates. In Aim 3, 13C-enriched phosphoenolpyruvate was utilized as a tracer to determine which metabolic pathways were activated during cell-free reactions. The 13C NMR data indicated glycolysis was activated as expected but also that gluconeogenic pathway was activated. Ethanol production was also detected, but from more than one carbon source. Insight such as this can guide future genetic modifications to the chassis organism and identify side products that may be wasting resources and decreasing product yields